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5 April 2017 Non-destructive monitoring of a prestressed bridge with a data-driven method
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Non-destructive vibration based methods can be used as diagnostic tool to identify damage in structures. Periodic inspections or permanent monitoring networks of sensors can indicate the emergence of possible damage occurring during the structure lifetime. Several methods have been proposed in literature for damage identification purposes. Some of them allow detecting the existence of damage, others provide information about its location as well. Data driven method are able to localize damage based solely on responses recorded on the structure without the need of a Finite Element model. Many of these methods are based on the detection of irregularities in the deformed shape of the structure: modal or operational shapes have been proposed to this purpose by different authors. The reliability of the methods proposed in literature is often verified on numerical models that, by their nature, cannot reproduce all the sources of uncertainties - environmental, operational, experimental - that affect responses recorded of the structure. The availability of data recorded on real structures provides precious material for the check of damage identification methods. In this paper the performance of the Interpolation Method for damage localization is investigated with reference to the real case study of a prestressed concrete road bridge, the S101 Bridge in Austria. The bridge, built in the early 1960, is a typical example of a European highway bridge. Responses to ambient vibration have been recorded both in the undamaged and in several different damage scenarios artificially inflicted to the bridge. Damage was introduced by lowering one of the bridge piers and by cutting prestressing tendons of one beam of the bridge deck.
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M. P. Limongelli, M. Tirone, and C. Surace "Non-destructive monitoring of a prestressed bridge with a data-driven method", Proc. SPIE 10170, Health Monitoring of Structural and Biological Systems 2017, 1017033 (5 April 2017);

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